Database community has made significant research efforts to
optimize query processing on GPUs in the past few years.
However,
we can hardly find that GPUs have been truly adopted in major warehousing production systems.
Preparing to merge GPUs to the warehousing systems,
we have identified and addressed several critical issues in a three-dimensional study
of warehousing queries on GPUs
by varying query characteristics, software techniques, and GPU hardware configurations.
We also propose an analytical model to understand and predict the query performance on GPUs. 
Based on our study, we present our performance insights for warehousing query execution on GPUs.
The objective of our work is to provide a comprehensive guidance
for GPU architects, software system designers, and database practitioners to narrow the speed gap
between the GPU kernel execution (the fast mode) and data transfer to prepare GPU execution (the slow mode) for high performance in processing data warehousing queries.
The GPU query engine developed in this work is open source to the public.


